# Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels

arXiv: https://arxiv.org/abs/2010.10397 · HTML: https://mlsanity.com/paper/2010.10397/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models](https://mlsanity.com/paper/2305.13873/) (2023)
- [The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who](https://mlsanity.com/paper/2304.14238/) (2023)
- [Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation](https://mlsanity.com/paper/2412.07338/) (2024)
- [Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning](https://mlsanity.com/paper/2209.09803/) (2022)
- [Discovering Differences in the Representation of People using Contextualized Semantic Axes](https://mlsanity.com/paper/2210.12170/) (2022)
- [Understanding Online Migration Decisions Following the Banning of Radical Communities](https://mlsanity.com/paper/2212.04765/) (2022)
- [Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence](https://mlsanity.com/paper/2301.06774/) (2023)
- [Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit](https://mlsanity.com/paper/2310.12186/) (2023)
- [How toxic is antisemitism? Potentials and limitations of automated toxicity scoring for antisemitic online content](https://mlsanity.com/paper/2310.04465/) (2023)
- [Personalized Interventions for Online Moderation](https://mlsanity.com/paper/2205.09462/) (2022)
- [The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussions](https://mlsanity.com/paper/2511.08592/) (2025)
- [Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop](https://mlsanity.com/paper/2601.05184/) (2026)
- [UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers](https://mlsanity.com/paper/2309.16275/) (2023)
- [Assessing and Refining ChatGPT's Performance in Identifying Targeting and Inappropriate Language: A Comparative Study](https://mlsanity.com/paper/2505.21710/) (2025)
- [Quantifying Feature Importance for Online Content Moderation](https://mlsanity.com/paper/2510.19882/) (2025)

## Abstract (arXiv)

When toxic online communities on mainstream platforms face moderation measures, such as bans, they may migrate to other platforms with laxer policies or set up their own dedicated websites. Previous work suggests that within mainstream platforms, community-level moderation is effective in mitigating the harm caused by the moderated communities. It is, however, unclear whether these results also hold when considering the broader Web ecosystem. Do toxic communities continue to grow in terms of their user base and activity on the new platforms? Do their members become more toxic and ideologically radicalized? In this paper, we report the results of a large-scale observational study of how problematic online communities progress following community-level moderation measures. We analyze data from r/The_Donald and r/Incels, two communities that were banned from Reddit and subsequently migrated to their own standalone websites. Our results suggest that, in both cases, moderation measures significantly decreased posting activity on the new platform, reducing the number of posts, active users, and newcomers. In spite of that, users in one of the studied communities (r/The_Donald) showed increases in signals associated with toxicity and radicalization, which justifies concerns that the reduction in activity may come at the expense of a more toxic and radical community. Overall, our results paint a nuanced portrait of the consequences of community-level moderation and can inform their design and deployment.